Probability & Statistics for Engineers & Scientists

by Ronald Walpole, Raymond Myers, Sharon Myers, Keying Ye

Overview

For junior/senior undergraduates taking probability and statistics as applied to engineering, science, or computer science.

This classic text provides a rigorous introduction to basic probability theory and statistical inference, with a unique balance between theory and methodology. Interesting, relevant applications use real data from actual studies, showing how the concepts and methods can be used to solve problems in the field. This revision focuses on improved clarity and deeper understanding.

This latest edition is also available in as an enhanced Pearson eText. This exciting new version features an embedded version of StatCrunch, allowing students to analyze data sets while reading the book.

Also available with MyStatLab

MyStatLab^™ is an online homework, tutorial, and assessment program designed to work with this text to engage students and improve results. Within its structured environment, students practice what they learn, test their understanding, and pursue a personalized study plan that helps them absorb course material and understand difficult concepts.

Note: You are purchasing a standalone product; MyLab™ & Mastering™ does not come packaged with this content. Students, if interested in purchasing this title with MyLab & Mastering, ask your instructor for the correct package ISBN and Course ID. Instructors, contact your Pearson representative for more information.

If you would like to purchase both the physical text and MyLab & Mastering, search for:

0134468910 / 9780134468914 Probability & Statistics for Engineers & Scientists, MyStatLab Update with MyStatLab plus Pearson eText — Access Card Package 9/e

Package consists of:

0134115856 / 9780134115856 Probability & Statistics for Engineers & Scientists, MyStatLab Update

0321847997 / 9780321847997 My StatLab Glue-in Access Card

032184839X / 9780321848390 MyStatLab Inside Sticker for Glue-In Packages

Product Details

ISBN-13:	9780134115856
Publisher:	Pearson Education
Publication date:	03/07/2016
Edition description:	Updated
Pages:	816
Product dimensions:	7.60(w) x 9.20(h) x 1.30(d)

Table of Contents

1. Introduction to Statistics and Data Analysis

• 1.1 Overview: Statistical Inference, Samples, Populations, and the Role of Probability
• 1.2 Sampling Procedures; Collection of Data
• 1.3 Measures of Location: The Sample Mean and Median
• Exercises
• 1.4 Measures of Variability
• Exercises
• 1.5 Discrete and Continuous Data
• 1.6 Statistical Modeling, Scientific Inspection, and Graphical Methods 19
• 1.7 General Types of Statistical Studies: Designed Experiment,
• Observational Study, and Retrospective Study
• Exercises

2. Probability

• 2.1 Sample Space
• 2.2 Events
• Exercises
• 2.3 Counting Sample Points
• Exercises
• 2.4 Probability of an Event
• 2.5 Additive Rules
• Exercises
• 2.6 Conditional Probability, Independence and Product Rules
• Exercises
• 2.7 Bayes' Rule
• Exercises
• Review Exercises
• 2.8 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

3. Random Variables and Probability Distributions

• 3.1 Concept of a Random Variable
• 3.2 Discrete Probability Distributions
• 3.3 Continuous Probability Distributions
• Exercises
• 3.4 Joint Probability Distributions
• Exercises
• Review Exercises
• 3.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

4. Mathematical Expectation

• 4.1 Mean of a Random Variable
• Exercises
• 4.2 Variance and Covariance of Random Variables
• Exercises
• 4.3 Means and Variances of Linear Combinations of Random Variables
• 4.4 Chebyshev's Theorem
• Exercises
• Review Exercises
• 4.5 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

5. Some Discrete Probability Distributions

• 5.1 Introduction and Motivation
• 5.2 Binomial and Multinomial Distributions
• Exercises
• 5.3 Hypergeometric Distribution
• Exercises
• 5.4 Negative Binomial and Geometric Distributions
• 5.5 Poisson Distribution and the Poisson Process
• Exercises
• Review Exercises
• 5.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

6. Some Continuous Probability Distributions

• 6.1 Continuous Uniform Distribution
• 6.2 Normal Distribution
• 6.3 Areas under the Normal Curve
• 6.4 Applications of the Normal Distribution
• Exercises
• 6.5 Normal Approximation to the Binomial
• Exercises
• 6.6 Gamma and Exponential Distributions
• 6.7 Chi-Squared Distribution
• 6.8 Beta Distribution
• 6.9 Lognormal Distribution (Optional)
• 6.10 Weibull Distribution (Optional)
• Exercises
• Review Exercises
• 6.11 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

7. Functions of Random Variables (Optional)

• 7.1 Introduction
• 7.2 Transformations of Variables
• 7.3 Moments and Moment-Generating Functions
• Exercises

8. Sampling Distributions and More Graphical Tools

• 8.1 Random Sampling and Sampling Distributions
• 8.2 Some Important Statistics
• Exercises
• 8.3 Sampling Distributions
• 8.4 Sampling Distribution of Means and the Central Limit Theorem
• Exercises
• 8.5 Sampling Distribution of S²
• 8.6 t-Distribution
• 8.7 F-Distribution
• 8.8 Quantile and Probability Plots
• Exercises
• Review Exercises
• 8.9 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

9. One- and Two-Sample Estimation Problems

• 9.1 Introduction
• 9.2 Statistical Inference
• 9.3 Classical Methods of Estimation
• 9.4 Single Sample: Estimating the Mean
• 9.5 Standard Error of a Point Estimate
• 9.6 Prediction Intervals
• 9.7 Tolerance Limits
• Exercises
• 9.8 Two Samples: Estimating the Difference Between Two Means
• 9.9 Paired Observations
• Exercises
• 9.10 Single Sample: Estimating a Proportion
• 9.11 Two Samples: Estimating the Difference between Two Proportions
• Exercises
• 9.12 Single Sample: Estimating the Variance
• 9.13 Two Samples: Estimating the Ratio of Two Variances
• Exercises
• 9.14 Maximum Likelihood Estimation (Optional)
• Exercises
• Review Exercises
• 9.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

10. One- and Two-Sample Tests of Hypotheses

• 10.1 Statistical Hypotheses: General Concepts
• 10.2 Testing a Statistical Hypothesis
• 10.3 The Use of P-Values for Decision Making in Testing Hypotheses
• Exercises
• 10.4 Single Sample: Tests Concerning a Single Mean
• 10.5 Two Samples: Tests on Two Means
• 10.6 Choice of Sample Size for Testing Means
• 10.7 Graphical Methods for Comparing Means
• Exercises
• 10.8 One Sample: Test on a Single Proportion
• 10.9 Two Samples: Tests on Two Proportions
• Exercises
• 10.10 One- and Two-Sample Tests Concerning Variances
• 10.11 Goodness-of-Fit Test
• 10.12 Test for Independence (Categorical Data)
• 10.13 Test for Homogeneity
• 10.14 Two-Sample Case Study Exercises Review Exercises
• 10.15 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

11. Simple Linear Regression and Correlation

• 11.1 Introduction to Linear Regression
• 11.2 The Simple Linear Regression Model
• 11.3 Least Squares and the Fitted Model
• Exercises
• 11.4 Properties of the Least Squares Estimators
• 11.5 Inferences Concerning the Regression Coefficients
• 11.6 Prediction
• Exercises
• 11.7 Choice of a Regression Model
• 11.8 Analysis-of-Variance Approach
• 11.9 Test for Linearity of Regression: Data with Repeated Observations 416
• Exercises
• 11.10 Data Plots and Transformations
• 11.11 Simple Linear Regression Case Study
• 11.12 Correlation
• Exercises
• Review Exercises
• 11.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

12. Multiple Linear Regression and Certain Nonlinear Regression Models

• 12.1 Introduction
• 12.2 Estimating the Coefficients
• 12.3 Linear Regression Model Using Matrices
• Exercises
• 12.4 Properties of the Least Squares Estimators
• 12.5 Inferences in Multiple Linear Regression
• Exercises
• 12.6 Choice of a Fitted Model through Hypothesis Testing
• 12.7 Special Case of Orthogonality (Optional)
• Exercises
• 12.8 Categorical or Indicator Variables
• Exercises
• 12.9 Sequential Methods for Model Selection
• 12.10 Study of Residuals and Violation of Assumptions
• 12.11 Cross Validation, C_p, and Other Criteria for Model Selection
• Exercises
• 12.12 Special Nonlinear Models for Nonideal Conditions
• Exercises
• Review Exercises
• 12.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

13. One-Factor Experiments: General

• 13.1 Analysis-of-Variance Technique
• 13.2 The Strategy of Experimental Design
• 13.3 One-Way Analysis of Variance: Completely Randomized Design (One-Way ANOVA)
• 13.4 Tests for the Equality of Several Variances
• Exercises
• 13.5 Multiple Comparisons
• Exercises
• 13.6 Comparing a Set of Treatments in Blocks
• 13.7 Randomized Complete Block Designs
• 13.8 Graphical Methods and Model Checking
• 13.9 Data Transformations In Analysis of Variance)
• Exercises
• 13.10 Random Effects Models
• 13.11 Case Study
• Exercises
• Review Exercises
• 13.12 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

14. Factorial Experiments (Two or More Factors)

• 14.1 Introduction
• 14.2 Interaction in the Two-Factor Experiment
• 14.3 Two-Factor Analysis of Variance
• Exercises
• 14.4 Three-Factor Experiments
• Exercises
• 14.5 Factorial Experiments for Random Effects and Mixed Models
• Exercises
• Review Exercises
• 14.6 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

15. 2^k Factorial Experiments and Fractions

• 15.1 Introduction
• 15.2 The 2^k Factorial: Calculation of Effects and Analysis of Variance
• 15.3 Nonreplicated 2^k Factorial Experiment
• Exercises
• 15.4 Factorial Experiments in a Regression Setting
• 15.5 The Orthogonal Design
• Exercises
• 15.6 Fractional Factorial Experiments
• 15.7 Analysis of Fractional Factorial Experiments
• Exercises
• 15.8 Higher Fractions and Screening Designs
• 15.9 Construction of Resolution III and IV Designs
• 15.10 Other Two-Level Resolution III Designs; The Plackett-Burman Designs
• 15.11 Introduction to Response Surface Methodology
• 15.12 Robust Parameter Design
• Exercises
• Review Exercises
• 15.13 Potential Misconceptions and Hazards; Relationship to Material in Other Chapters

16. Nonparametric Statistics

• 16.1 Nonparametric Tests
• 16.2 Signed-Rank Test
• Exercises
• 16.3 Wilcoxon Rank-Sum Test
• 16.4 Kruskal-Wallis Test
• Exercises
• 16.5 Runs Test
• 16.6 Tolerance Limits
• 16.7 Rank Correlation Coefficient
• Exercises
• Review Exercises

17. Statistical Quality Control

• 17.1 Introduction
• 17.2 Nature of the Control Limits
• 17.3 Purposes of the Control Chart
• 17.4 Control Charts for Variables
• 17.5 Control Charts for Attributes
• 17.6 Cusum Control Charts
• Review Exercises

18. Bayesian Statistics

• 18.1 Bayesian Concepts
• 18.2 Bayesian Inferences
• 18.3 Bayes Estimates Using Decision Theory Framework
• Exercises
• Bibliography

A. Statistical Tables and Proofs B. Answers to Odd-Numbered Non-Review Exercises Index